Influenza virus is a member of Orthomyxoviridae family. There are three subtypes of influenza viruses designated A, B, and C. The influenza virion contains a segmented negative-sense RNA genome, encoding, among other proteins, hemagglutinin (HA) and neuraminidase (NA). Influenza virus infection is initiated by the attachment of the virion surface HA protein to a sialic acid-containing cellular receptor (glycoproteins and glycolipids). The NA protein mediates processing of the sialic acid receptor, and virus penetration into the cell depends on HA-dependent receptor-mediated endocytosis. So fair, chemical analogs of the receptor have not been successful as viral-entry blockers. Current treatment options include therapeutic antibodies, small-molecules drugs and vaccination. These therapies allow protection against circulating subtypes, but may not protect against newly emerging strains. Hence, general or quickly adaptable solutions for cheap treatment options represent a constant need. Additionally, in order to rapidly diagnose early whether a patient indeed suffers from influenza, sensitive diagnostics are desirable, as treatment at the onset of the infection have been shown to be more efficient. Influenza presents a serious public-health challenge and new therapies are needed to combat viruses that are resistant to existing antivirals or escape neutralization by the immune system.
Small (4-12 kDa) binding proteins have the potential to bridge the gap between monoclonal antibodies and small molecule drugs, with advantages of stability and chemical synthesis over monoclonal antibodies, and in selectivity and designability over small molecules. Directed evolution has been used starting from naturally occurring small protein scaffolds to generate new binding proteins. While powerful, such approaches have limitations: they cannot modify the overall shape of the starting scaffold protein(s), they can only sample a very small fraction of sequence space, and naturally occurring disulfide mini-proteins can be difficult to express. Computational protein design has the potential to overcome these limitations by efficiently sampling both shape and sequence space on a much larger scale, and generating readily producible proteins, as recently demonstrated by the design of stapled mini protein scaffolds with a wide range of shapes. Despite this potential, the high cost to synthesize genes for each designed protein has generally limited testing to small numbers (tens) of designs for an one application, which is too few to systematically explore the capability of this approach and to provide feedback to improve the computational model.